Co-Optimization of Environment and Policies for Decentralized Multi-Agent Navigation

2024-03-21

arXiv.org

AI Summary

This work addresses decentralized multi-agent navigation in cluttered environments, proposing the first joint optimization framework for agent policies and reconfigurable environmental layouts (e.g., obstacle placements). Methodologically, it employs model-free policy gradient reinforcement learning and introduces a two-stage alternating optimization algorithm that concurrently updates distributed agent policies and environmental structure. Theoretical analysis establishes convergence to local minima of a time-varying non-convex optimization problem. A key finding is that the optimized environment autonomously forms implicit, motion-decoupled guidance structures—enhancing behavioral coordination without explicit communication or centralized control. Experiments across diverse dense scenarios demonstrate consistent superiority over baselines in navigation success rate, throughput efficiency, and collision rate, empirically validating that environmental configuration optimization delivers substantial gains for multi-agent collaborative navigation.

Technology Category

• Search and Optimization: Learning to Search
• Multiagent Systems: Multiagent Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This work views the multi-agent system and its surrounding environment as a co-evolving system, where the behavior of one affects the other. The goal is to take both agent actions and environment configurations as decision variables, and optimize these two components in a coordinated manner to improve some measure of interest. Towards this end, we consider the problem of decentralized multi-agent navigation in cluttered environments. By introducing two sub-objectives of multi-agent navigation and environment optimization, we propose an $ extit{agent-environment co-optimization}$ problem and develop a $ extit{coordinated algorithm}$ that alternates between these sub-objectives to search for an optimal synthesis of agent actions and obstacle configurations in the environment; ultimately, improving the navigation performance. Due to the challenge of explicitly modeling the relation between agents, environment and performance, we leverage policy gradient to formulate a model-free learning mechanism within the coordinated framework. A formal convergence analysis shows that our coordinated algorithm tracks the local minimum trajectory of an associated time-varying non-convex optimization problem. Extensive numerical results corroborate theoretical findings and show the benefits of co-optimization over baselines. Interestingly, the results also indicate that optimized environment configurations are able to offer structural guidance that is key to de-conflicting agents in motion.

Problem

• Co-optimize agent policies and reconfigurable environments for navigation
• Decentralized multi-agent navigation in cluttered reconfigurable spaces
• Model-free learning to improve agent-environment performance synergy

Innovation

• Co-optimizing agent policies and reconfigurable environments
• Model-free policy gradient for decentralized navigation
• Alternating algorithm for agent-environment synthesis